5 Modelling labour force participation

This section will explore the relationship between various health measures and labour force participation. In the first instance, we address a series of challenges that arise in attempting to estimate this relationship. The following two sections outline the modelling approaches adopted in this study.

5.1 Methodological challenges

A relationship of particular interest is that between health and participation. However, it is difficult to establish robustly whether there is a direct relationship between health status and labour force participation, as health and retirement are jointly determined; and finding an appropriate measure of health can be problematic. Longitudinal data with multiple health measures are advantageous, as the change to retirement can be tracked over time for each individual, together with any associated changes in health status.

An issue faced when attempting to isolate the determinants of labour force participation is the potential influence of factors we cannot observe on an individual's health, wealth and labour market decisions.

Examples of unobservable differences between people, which may generate a spurious relationship, include heterogeneity of family background, education, culture and preferences. Early-childhood socioeconomic status and health conditions, as well one's further upbringing and life events, are important in human capital development and health investment decisions over the lifecycle (Tubeuf, et al., 2012). These shape many outcomes in older ages, including labour-market engagement and health status. They also influence cognitive functioning and other measures associated with successful ageing, for example engagement in voluntary work, caregiving and social networks (Brandt, et al., 2012).

Figure 6 presents a schematic depiction of some of the complex linkages between health and participation. This paper focuses primarily on the contemporaneous relationship between health and work status, denoted by the bolded arrow, controlling for the confounding factors indicated on the periphery. Given the longitudinal nature of the data, an individual’s history of health status over the sample frame can be taken into account, in addition to contemporaneous health status. However, the full effects of the trajectory of health throughout the life course, and previous interactions between health and other components of human capital (for example, education) cannot be captured without a significantly longer panel, or a retrospective study.

Figure 6 - A schematic view of health and work interdependencies

Source: Enright and Scobie (2010)

Another source of heterogeneity is individual habits and preferences. Preferences and habits which influence behaviour are common factors which affect outcomes such as wealth accumulation, health and participation. A specific example is time preference, which may be theoretically related to health as follows. As formalised by Grossman (1972), health can be seen as an endogenous capital stock which is inherited at birth, depreciates with age, and can be augmented by investment in health over the life-cycle. Investments in this stock are a product of inputs such as the individual’s time, as well as consumable goods and services such as medical care and diet. The efficiency of health production is influenced by “environmental” factors such as education; more educated and informed individuals are assumed to be more “efficient producers” of good health. A key difference between health and traditional human capital, such as education, is that health capital may be subject to adverse shocks. Increased investment in health leads to a lower susceptibility to shocks. Myopic individuals may under-invest in health capital, as well as in human capital. Consequently, we observe them to have a lower average level of health, have a higher susceptibility to health shocks, and a lower lifetime propensity to work, regardless of any causal effect of health on participation.

This kind of unobserved heterogeneity seems especially relevant for older individuals, as such attitudes, habits and preferences may be well cemented, or their effects have materialised, by the time the individual reaches the age of interest here. This further highlights the importance of attempting to account for these effects. Moreover, this distinction between a causal or spurious relationship is important for policy.

If there were, indeed, a causal link between health and participation, increased public provision of health care may well be effective in increasing and/or lengthening spells of participation. However, if such a link were rebutted in favour of the unobserved common factors hypothesis (ie, unobserved heterogeneity), attempting to increase labour force participation by increasing health funding may be futile, as the genuine underlying cause would be left unaddressed. In this case, a more appropriate policy response would perhaps be policies targeted toward those who may be likely to have sub-optimal outcomes in a range of areas, including, but not limited to, health, wealth and participation, owing to unobserved preferences and habits accumulated over the life course.

To gain a more robust indicator of a relationship, one may examine how changes in health are related to labour force participation, as opposed to examining health and participation at one point in time. This is one of the strengths of longitudinal data. However, it must be noted that this method in itself does not provide definitive answers to the direction of causality.